JCLALJCLAL is a general purpose framework developed in Java for Active Learning.
Stars: ✭ 22 (-37.14%)
Mutual labels: semi-supervised-learning, active-learning
Adversarial AutoencodersTensorflow implementation of Adversarial Autoencoders
Stars: ✭ 215 (+514.29%)
Mutual labels: semi-supervised-learning
Mixmatch PytorchPytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)
Stars: ✭ 120 (+242.86%)
Mutual labels: semi-supervised-learning
Stylealign[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition
Stars: ✭ 172 (+391.43%)
Mutual labels: semi-supervised-learning
SnowballImplementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)
Stars: ✭ 131 (+274.29%)
Mutual labels: semi-supervised-learning
VoskVOSK Speech Recognition Toolkit
Stars: ✭ 182 (+420%)
Mutual labels: semi-supervised-learning
IctCode for reproducing ICT ( published in IJCAI 2019)
Stars: ✭ 107 (+205.71%)
Mutual labels: semi-supervised-learning
Good PapersI try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
Stars: ✭ 248 (+608.57%)
Mutual labels: semi-supervised-learning
Triple GanSee Triple-GAN-V2 in PyTorch: https://github.com/taufikxu/Triple-GAN
Stars: ✭ 203 (+480%)
Mutual labels: semi-supervised-learning
Accel Brain CodeThe purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
Stars: ✭ 166 (+374.29%)
Mutual labels: semi-supervised-learning
Deep Sad PytorchA PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
Stars: ✭ 152 (+334.29%)
Mutual labels: semi-supervised-learning
UdaUnsupervised Data Augmentation (UDA)
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Mutual labels: semi-supervised-learning
Graph Adversarial LearningA curated collection of adversarial attack and defense on graph data.
Stars: ✭ 188 (+437.14%)
Mutual labels: semi-supervised-learning
CleanlabThe standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.
Stars: ✭ 2,526 (+7117.14%)
Mutual labels: semi-supervised-learning
Improvedgan PytorchSemi-supervised GAN in "Improved Techniques for Training GANs"
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Mutual labels: semi-supervised-learning
Adversarial textCode for Adversarial Training Methods for Semi-Supervised Text Classification
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Mutual labels: semi-supervised-learning
Cct[CVPR 2020] Semi-Supervised Semantic Segmentation with Cross-Consistency Training.
Stars: ✭ 171 (+388.57%)
Mutual labels: semi-supervised-learning
DeFMO[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Stars: ✭ 144 (+311.43%)
Mutual labels: semi-supervised-learning
